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Early Parkinson's Disease Detection and Classification using Machine Learning and Neutrosophic Sets

Neutrosophic Sets and systems 2025年 82卷 873-886页

作者： Oudah, Hasan H. Metwaly, Ahmed A. Eassa, Mohamed Abdelhafeez, Ahmed Nagm, Ahmad M. Salama, Ahmed S. College of Arts Department of Journalism Ahl Al Bayt University Karbala Iraq Department of Computer Science Faculty of Computers and Informatics Zagazig University Zagazig44519 Egypt Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan Department of Computer Engineering and Electronics Cairo Higher Institute for Engineering Computer Science and Management New Cairo Egypt

Optimizing therapy and rehabilitation for Parkinson's disease (PD) requires early identification and precise evaluation of the illness's course. However, there is disagreement about the best way to use gait analysis to categorize the severity of motor symptoms and identify early-stage Parkinson's disease. The precision of machine learning (ML) models in identifying early and intermediate stages of Parkinson's disease was assessed in this study. Six ML models are used in this study for the prediction of PD. Different metrics are used to evaluate ML models such as accuracy, precision, recall, f1-score, and AUC score. Then we propose a multi-criteria decision-making methodology (MCDM) to evaluate the ML models and select the best one. Two MCDM methods are used such as CRITIC method to compute the criteria weights and the TOPSIS method to rank the alternatives. These methods are used under the bipolar neutrosophic sets (BNSs) to deal with uncertainty information. The results show the support vector machine is the best ML model for the prediction of PD. © 2025, University of New Mexico. All rights reserved.

关键词： Neurodegenerative diseases

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A systematic Literature Review on the Security Attacks and Countermeasures Used in Graphical Passwords

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IEEE Access 2024年 12卷 53408-53423页

作者： Por, Lip Yee Ng, Ian Ouii Chen, Yen-Lin Yang, Jing Ku, Chin Soon Universiti Malaya Faculty of Computer Science and Information Technology Department of Computer System and Technology Kuala Lumpur50603 Malaysia National Taipei University of Technology Department of Computer Science and Information Engineering Taipei106344 Taiwan Universiti Tunku Abdul Rahman Department of Computer Science Kampar31900 Malaysia

This systematic literature review delves into the dynamic realm of graphical passwords, focusing on the myriad security attacks they face and the diverse countermeasures devised to mitigate these threats. The core objective of this paper is to identify existing security threats to graphical password schemes and the corresponding countermeasures developed to mitigate these attacks. The study process begins by identifying the usable databases and search engines to identify all the relevant resources. The inclusion and exclusion criteria were carefully selected to prioritize the study, focusing mostly on attacks and countermeasures related to graphical password schemes between 2009 and 2023. After thorough identification and selection progress, 59 studies met all the criteria. Among these studies, 47 mentioned shoulder surfing as a threat to graphical password schemes, while 20 discussed brute force attacks. Additionally, there were 21 papers on dictionary attacks, 13 on smudge attacks, spyware attacks, and social engineering, and 19 that discussed guessing attacks as threats to graphical password schemes. Furthermore, the papers identified several other attacks, including frequency of occurrence analysis attacks, video recording, eavesdropping, computer vision, sonar, and image gallery attacks, with the corresponding numbers of papers being 9, 17, 5, 2, 2, and 1, respectively. The results also highlight the countermeasures proposed in the study papers to mitigate the aforementioned attacks. Among the various countermeasures identified, most revolve around randomization, obfuscation, and password space complexity as the most commonly used techniques for enhancing the security of graphical password schemes. © 2013 IEEE.

关键词： Authentication

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Advancing Bankruptcy Forecasting With Hybrid Machine Learning Techniques: Insights From an Unbalanced Polish Dataset

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IEEE Access 2024年 12卷 9369-9381页

作者： Ainan, Ummey Hany Por, Lip Yee Chen, Yen-Lin Yang, Jing Ku, Chin Soon Universiti Malaya Faculty of Computer Science and Information Technology Department of Computer System and Technology Kuala Lumpur50603 Malaysia National Taipei University of Technology Department of Computer Science and Information Engineering Taipei106344 Taiwan Universiti Tunku Abdul Rahman Department of Computer Science Kampar31900 Malaysia

The challenge of bankruptcy prediction, critical for averting financial sector losses, is amplified by the prevalence of imbalanced datasets, which often skew prediction models. Addressing this, our study introduces the innovative hybrid model XGBoost+ANN, designed to leverage the strengths of both ensemble learning and artificial neural networks. This model integrates a comprehensive set of features with parameters optimized through genetic algorithms, eschewing traditional feature selection approaches. Our research focuses on an unbalanced dataset of Polish companies and reveals that the XGBoost+ANN model, in particular, exhibits outstanding performance. Optimized using genetic algorithms and without feature selection, this model achieved the highest AUC (0.958), sensitivity (0.752), and accuracy (0.983) scores, surpassing other models in our study. This remarkable outperformance, along with the robust results, marks a substantial advancement in the field of bankruptcy prediction. It underscores the efficacy of our approach in addressing the persistent challenge of data imbalance, offering a more reliable and accurate solution for financial risk assessment. © 2013 IEEE.

关键词： Feature extraction

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Load Balancing in Cloud Computing: Techniques, Challenges and Future Prospects 1

Load Balancing in Cloud Computing: Techniques, Challenges an...

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1st International Conference on Computing, sciences and Communications, ICCSC 2024

作者： Agarwal, Srishti Singh, Jitendra Ansari, M.I.H. SRM Institute of Science& Technology Department of Computer Science& Engineering U.P. Ghaziabad201204 India SRM Institute of Science & Technology Delhi NCR Campus CSE department Modinagar U.P. Ghaziabad201204 India Meerut Institute of Technology Department of Computer Science & Engineering U.P. Meerut India

ISBN: (纸本)9798350353648

Load balancing is a critical aspect of optimizing resource utilization and performance in cloud computing environments. This paper begins by introducing the fundamental concepts of load balancing and underscoring its significance in achieving scalability, efficiency, and user satisfaction within cloud ecosystems & presents a comprehensive exploration of load balancing techniques in cloud environment. The objectives of the paper are to unveil a spectrum of load balancing techniques, discuss challenges inherent to load distribution, and identify potential future prospects for this evolving field. A comparative analysis of the various approaches highlights their respective strengths and weaknesses, considering parameters like performance, scalability, and implementation complexity. With an extensive survey of the literature, this paper provides researchers and practitioners with a holistic perspective on load balancing in cloud computing. © 2024 IEEE.

关键词： Cloud Computing Future Prospects Load Balancing Resource Optimization Scalability

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AMTLUS: Attention-guided multi-task learning with uncertainty estimation in skin lesion segmentation and classification

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Multimedia Tools and Applications 2024年第37期83卷 84885-84909页

作者： Kasukurthi, Aravinda Davuluri, Rajya Lakshmi Department of Computer Science & amp Engineering JNTU-GV Andhra Pradesh Vizianagaram India Department of Computer Science and Business System R.V.R. & ampJ.C. College of Engineering Andhra Pradesh Guntur India

Skin lesion segmentation and classification from dermoscopic images have emerged as pivotal research topics, playing vibrant role in early detection and diagnosis of skin diseases, including melanoma. Previous studies have employed various deep learning models for skin lesion segmentation and classification, enabling the automatic learning of complex and discriminative features from dermoscopic images. However, inherent challenges arise due to the variance in skin lesion shape, size, and contrast, leading to intrinsic limitations of former models, such as Isolated Representation Learning, Uniform Attention, Limited Model Generalization, Reduced Model Interpretability, and Uncertainty. To address these limitations and propel the field forward, this paper introduces a novel frameworkcalled AMTLUS that leverages Multi-Task Learning (MTL) in conjunction with deep Attention Mechanisms and Uncertainty Estimation. The integration of MTL facilitates joint training of segmentation and classification tasks, enabling shared representation learning and efficient utilization of data. Incorporating attention mechanisms dynamically focuses on informative regions within dermoscopic images, improving segmentation accuracy and feature extraction for classification. Uncertainty estimation techniques quantify model confidence, offering probabilistic interpretations for improved reliability and interpretability. Our widespread experiments conducted on the ISIC-2016 dataset demonstrate superior accuracy and reliability, showcasing the proposed model’s capability to identify challenging cases. This deep learning framework represents a significant advancement in automated skin lesion analysis, enhancing early detection and diagnosis of skin diseases, including melanoma. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Image enhancement

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Design and development of political rider competitive swarm optimiser enabled deep learning model for air quality detection

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International Journal of Intelligent systems Technologies and Applications 2024年第1期22卷 77-104页

作者： Patil, Deepika Dadasaheb Thanuja, T.C. Melinamath, Bhuvaneshwari C. Department of Computer Science and Engineering Vishveshvaraya Technological University Belgavi India Sanjay Ghodawat University Kolhapur India Department of VLSI and Embedded System Visvesvaraya Technological University Belagavi India Department of Computer Science and Engineering BMSIT&M College Bangalore India

The air quality prediction process is a more significant one for air pollution prevention and management because air pollution becomes crueller. The precise identification of air quality has become a more significant concern for controlling air pollution. Recently, the weight of particulate matter (PM) on the human physical condition has become an important research area. In this paper, the political rider competitive swarm optimiser (PRCSO)-based deep recurrent neural network (DRNN) algorithm is devised for air quality and carbon monoxide prediction. The missing value imputation scheme is employed to perform pre-processing. Moreover, technical indicators and location information are extracted for the prediction process. The DRNN is employed for prediction, which is trained by the PRCSO and the training process is performed based on every location independently. The PRCSO-based DRNN outperforms existing techniques in terms of mean square error (MSE) of 0.0313, and mean absolute percentage error (MAPE) of 3.08%. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Carbon monoxide

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Automatic Transportation Mode Classification Using a Deep Reinforcement Learning Approach With Smartphone Sensors

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IEEE Access 2024年 12卷 514-533页

作者： Taherinavid, Siavash Moravvej, Seyed Vahid Chen, Yen-Lin Yang, Jing Ku, Chin Soon Yee, Por Lip Iran University of Science and Technology School of Civil Engineering Tehran13114-16846 Iran Isfahan University of Technology Department of Electrical and Computer Engineering Isfahan84156-83111 Iran National Taipei University of Technology Department of Computer Science and Information Engineering Taipei106344 Taiwan Universiti Malaya Faculty of Computer Science and Information Technology Department of Computer System and Technology Kuala Lumpur50603 Malaysia Universiti Tunku Abdul Rahman Department of Computer Science Kampar31900 Malaysia

The increasing dependence on smartphones with advanced sensors has highlighted the imperative of precise transportation mode classification, pivotal for domains like health monitoring and urban planning. This research is motivated by the pressing demand to enhance transportation mode classification, leveraging the potential of smartphone sensors, notably the accelerometer, magnetometer, and gyroscope. In response to this challenge, we present a novel automated classification model rooted in deep reinforcement learning. Our model stands out for its innovative approach of harnessing enhanced features through artificial neural networks (ANNs) and visualizing the classification task as a structured series of decision-making events. Our model adopts an improved differential evolution (DE) algorithm for initializing weights, coupled with a specialized agent-environment relationship. Every correct classification earns the agent a reward, with additional emphasis on the accurate categorization of less frequent modes through a distinct reward strategy. The Upper Confidence Bound (UCB) technique is used for action selection, promoting deep-seated knowledge, and minimizing reliance on chance. A notable innovation in our work is the introduction of a cluster-centric mutation operation within the DE algorithm. This operation strategically identifies optimal clusters in the current DE population and forges potential solutions using a pioneering update mechanism. When assessed on the extensive HTC dataset, which includes 8311 hours of data gathered from 224 participants over two years. Noteworthy results spotlight an accuracy of 0.88±0.03 and an F-measure of 0.87±0.02, underscoring the efficacy of our approach for large-scale transportation mode classification tasks. This work introduces an innovative strategy in the realm of transportation mode classification, emphasizing both precision and reliability, addressing the pressing need for enhanced classification mechanisms in an eve

关键词： Smartphones

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Development of a machine learning model for predicting abnormalities of commercial airplanes

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Data science and Management 2024年第3期7卷 256-265页

作者： Rossi Passarella Siti Nurmaini Muhammad Naufal Rachmatullah Harumi Veny Fara Nissya Nur Hafidzoh Department of Computer Engineering Faculty of Computer ScienceUniversitas Sriwijaya30662Indonesia Intelligent System Research Group(ISysRG) Faculty of Computer ScienceUniversitas Sriwijaya30662Indonesia Department of Informatics Faculty of Computer ScienceUniversitas Sriwijaya30662Indonesia College of Engineering Studies Universiti Teknologi MARA(UiTM)Shah Alam40450Malaysia

Airplanes are a social necessity for movement of humans,goods,and *** are generally safe modes of transportation;however,incidents and accidents occasionally *** prevent aviation accidents,it is necessary to develop a machine-learning model to detect and predict commercial flights using automatic dependent surveillance–broadcast *** study combined data-quality detection,anomaly detection,and abnormality-classification-model *** research methodology involved the following stages:problem statement,data selection and labeling,prediction-model development,deployment,and *** data labeling process was based on the rules framed by the international civil aviation organization for commercial,jet-engine flights and validated by expert commercial *** results showed that the best prediction model,the quadratic-discriminant-analysis,was 93%accurate,indicating a“good fit”.Moreover,the model’s area-under-the-curve results for abnormal and normal detection were 0.97 and 0.96,respectively,thus confirming its“good fit”.

关键词： Automatic dependent surveillance-broadcast data Commercial airplanes accident Data-labeling Machine learning Prediction model

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Modeling the Percentage of NEET in Indonesia with Spatial Cauchy Regression through the Bayesian Analysis Approach

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IAENG International Journal of Applied Mathematics 2024年第7期54卷 1288-1295页

作者： Rantini, Dwi Fakhruzzaman, Muhammad Noor Ningrum, Ratih Ardiati Othman, Fazidah Choir, Achmad Syahrul Ramadan, Arip Alya, Najma Attaqiya Putri, Elfira Rahma Pratama, Muhammad Alfian Engineering Department Data Science Technology Study Program Faculty of Advanced Technology and Multidiscipline Universitas Airlangga Indonesia Engineering Department Data Science Technology Study Program Faculty of Advanced Technology andMultidiscipline Universitas Airlangga Indonesia Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Malaysia Statistical Computing StudyProgram Politeknik Statistika STIS Indonesia Information System Study Program School of Industrial and System Engineering Telkom University Indonesia

Indonesia has entered a period of demographic bonus. Human resources must be optimized. The number of children who do not in employment, education or training (NEET) in each province needs attention. Several factors that could contribute to the decline in the NEET percentage are literacy rates and the number of adolescents with computer skills. Increasing these two factors is believed to be able to reduce the percentage of NEET in every province in Indonesia. To find out the relationship between these two factors and how much influence they have on the percentage of NEET, this research is modeled by Cauchy regression and includes spatial effects. The results of the analysis show that the best model is when the spatial effect is modeled by Fernandez Steel Skew Normal conditionally autoregressive (FSSN CAR). This result is seen from the smallest value of the Watanabe Akaike Information Criterion (WAIC) in this model, which is 190.5. The parameter estimated shows that the higher the literacy rate and the number of adolescents with computer skills, the lower the percentage of NEET in each province in Indonesia. The results of this research can be useful for the Indonesian government to increase the number of educational facilities related to these two factors. © (2024), (International Association of Engineers). All rights reserved.

关键词： Bayesian CAR Cauchy Education NEET

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Predictive Modeling of Child Identity Risk using Machine and Deep Learning: A systematic Literature Review 2

Predictive Modeling of Child Identity Risk using Machine and...

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2nd International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2024

作者： Dighe, Akshada Garg, Bindu Department of Computer Engineering BVDUCOE Pune India Department of Computer Science and Business System BVDUCOE Pune India

ISBN: (纸本)9798331541217

Intellectual disability (ID), a developmental condition often stemming from prenatal or postnatal factors, exerts a profound influence on individuals' lives and necessitates timely intervention. Conventional screening methods are resource-intensive and demand specialized medical expertise. This study introduces a novel, non-invasive approach to ID screening by meticulously analyzing facial features and vocal characteristics extracted from interview *** the extraction of facial measurements and speech patterns, we devised a Craniofacial Variability Index (CVI) to assess the likelihood of ID. Furthermore, advanced machine learning techniques were strategically employed to augment the predictive accuracy of this index. Our empirical findings unequivocally demonstrate promising results, with an accuracy rate reaching up to 80% when synergizing CVI scores and phonetic features. This research underscores the potential of a non-invasive, AI-powered screening instrument for ID, offering a more accessible and efficient alternative to prevailing practices. Future investigations will concentrate on refining the model and validating its efficacy across diverse populations. © 2024 IEEE.

关键词： Adversarial machine learning

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